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[short] In Search of Needles in a 10M Haystack: Recurrent Memory Finds What LLMs Miss
The paper introduces BABILong benchmark to evaluate transformer models on processing long documents. Fine-tuning GPT-2 with memory augmentations enables handling tasks with up to elements, a significant advancement.
https://arxiv.org/abs//2402.10790
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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By Igor Melnyk
5
33 ratings
The paper introduces BABILong benchmark to evaluate transformer models on processing long documents. Fine-tuning GPT-2 with memory augmentations enables handling tasks with up to elements, a significant advancement.
https://arxiv.org/abs//2402.10790
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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